1. Field of the Invention
The invention relates to a method for the optical detection of objects or object streams, such as resting objects or moving object streams, wherein their surfaces are capable of reflecting or scattering light, and which objects can exhibit, or can form, or can generate self-affine or self-similar or fractal patterns or structures on the surfaces or in themselves according to the preamble of claim 1.
2. Description of the Related Art Including Information Disclosed Under 37 CFR 1.97 and 1.98.
Self-similar or self-affine or fractal formations or structures are testimonies of an omnipresent structure type, represented in the surrounding objects and the processes in the nature. The Koch curve or the Sierpinski triangle are cited as classic self-similar or, respectively, fractal formations in geometric-mathematical representation. A structure is designated as exactly self-similar if it can be subdivided into arbitrarily small parts, of which each part is a small copy of the complete structure, whereby it is important that the small parts are generated from the complete structure by a similitude transformation. Two objects are in this situation similar if they exhibit, disregarding their size, the same shape; transformations between the objects, where the transformations include connections of scalings, rotations, and translations, are in this case similitude transformations.
Natural self-affine structures appear less ideally in the environment; they are more or less stochastically modulated and in particular the defining property of the invariance to scaling is often limited to certain size ranges of the structure-forming features, in this case one calls the situation to be self-affine to self-similar. Similarly, several different self-affine or fractal structures can appear superposed as multi-fractals. The fractal dimension or self-similar dimension (FD) is employed, among others, as measurement size or characteristic size both for the mathematical-ideal fractals as well as for natural fractals, wherein the fractal dimension or self-similar dimension (FD) appears as a rational number and is a measurement of the degree of the complexity of the connections or, respectively, the width of variation of two values, wherein the self-similar dimension FD follows a low of exponentiation.
A measurement-technical use of the self-similar dimension has up to now hardly found any application in the quality control and the process control. In fact, the self-affinity or, respectively, fractals appear in many natural patterns, however in a variety of phenotypes, which are viewed as measurement-technically impeding the measurement, such that the evaluation of fracture faces, of pore patterns or of scar patterns, of bubbles in foams, of folding patterns, or of wetting faces can in most cases only be performed according to the appearance to the eye. In principle, such a capturing by way of a picture data processing with a video camera and a storage medium would be possible, however in connection with a very specific system adaptation and thus, at the end, an uninteresting relationship between costs and use value. Furthermore, an overirradiation of the feature by secondary light in case of connected images of surfaces generates also an information loss for a subsequent picture data processing by way of a video camera. For this purpose, the fracture faces or textures, which are frequently carriers of self-affine patterns to self-similar patterns, are very much subject to interference.
A device for the large-area optical topography measurement for investigating diffuse-reflecting surfaces by way of projected patterns on the object surfaces has become known from the German Printed Patent document DE-U1-9301901. Furthermore, a method for the measurement of the roughness of the surface of work pieces has become known from the German Printed Patent document DE-A1-3532690, wherein the surface of irradiated with light and the distribution of the intensity of the reflected light is measured with a converter and the signal of roughness is calculated therefrom. Furthermore, it is known from P. Pfeifer “Fractal Dimension as Working Tool for Surface-Roughness Problems” in Applications of Surface Science 18, 1984, pp. 146-164, Amsterdam, that the fractal dimension can be employed for determining the roughness of surfaces.